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Serverless Machine Learning with Amazon Redshift ML

Serverless Machine Learning with Amazon Redshift ML

By : Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi
5 (3)
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Serverless Machine Learning with Amazon Redshift ML

Serverless Machine Learning with Amazon Redshift ML

5 (3)
By: Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi

Overview of this book

Amazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions. This practical guide will help developers and data professionals working with Amazon Redshift data warehouses to put their SQL knowledge to work for training and deploying machine learning models. The book begins by helping you to explore the inner workings of Redshift Serverless as well as the foundations of data analytics and types of data machine learning. With the help of step-by-step explanations of essential concepts and practical examples, you’ll then learn to build your own classification and regression models. As you advance, you’ll find out how to deploy various types of machine learning projects using familiar SQL code, before delving into Redshift ML. In the concluding chapters, you’ll discover best practices for implementing serverless architecture with Redshift. By the end of this book, you’ll be able to configure and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale.
Table of Contents (19 chapters)
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1
Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
5
Part 2:Getting Started with Redshift ML
11
Part 3:Deploying Models with Redshift ML

Creating your first machine learning model

Finally, we will now build our first ML model to predict customer churn events. As this is our first machine learning model, let’s use the simple CREATE MODEL command. This option uses Amazon SageMaker Autopilot, which means, without the heavy lifting of building ML models, you simply provide a tabular dataset and select the target column to predict and SageMaker Autopilot automatically explores different solutions to find the best model. This includes data preprocessing, model training, and model selection and deployment. AutoMode is the default mode:

  1. Redshift ML shares training data and artifacts between Amazon Redshift and SageMaker through an S3 bucket. If you don’t have one already, you will need to create an S3 bucket. To do this, navigate to the Amazon S3 console and click on the Create bucket button:
Figure 5.3 – S3 console

Figure 5.3 – S3 console

  1. On the Create bucket page, under Bucket name...

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